Production Batch Size Research Articles

Model Penentuan Ukuran Batch Produksi dan Bufferstock untuk Sistem Produksi Mengalami Penurunan Kinerja dengan Mempertimbangkan Perubahan Order Awal

This study develops a model that involves information the preliminary order. At first, the manufacturer provides the preliminary order for the coming week (five days) varies from day to day and is received on Friday. Change in the preliminary order for a given day is announced one day before and this is viewed as it occurs randomly. Moreover, production systems experience performance degradation (deterioration). Status of the production process shifts from in control to out of control that is identified by the last inspection. Inspection is done by sampling. At the time of the status of out of control the probability of producing non-conforming system component that is charged to the restoration cost and warranty costs.This paper is looking for a solution for determining the production batch size and the buffer stock to reduce total cost. The decision variables are production run period (T) and buffer factor (m). Having obtained the variables T and m, then the variable production batch size (QT) and the buffer stock (BT) can be determined sequentially. Heuristic methods used are Silver-Meal (SM) and Least Unit Cost (LUC) to obtain a solution for each model. Numerical examples are given to demonstrate the performance of the models. From the numerical results, it appears that LUC method is better than SM method.

Two Echelon Supply Chain Integrated Inventory Model for Similar Products: A Case Study

The purpose of this paper is to develop a mathematical model towards minimization of total cost across echelons in a multi-product supply chain environment. The scenario under consideration is a two-echelon supply chain system with one manufacturer, one retailer and M products. The retailer faces independent Poisson demand for each product. The retailer and the manufacturer are closely coupled in the sense that the information about any depletion in the inventory of a product at a retailer’s end is immediately available to the manufacturer. Further, stock-out is backordered at the retailer’s end. Thus the costs incurred at the retailer’s end are the holding costs and the backorder costs. The manufacturer has only one processor which is time shared among the M products. Production changeover from one product to another entails a fixed setup cost and a fixed set up time. Each unit of a product has a production time. Considering the cost components, and assuming transportation time and cost to be negligible, the objective of the study is to minimize the expected total cost considering both the manufacturer and retailer. In the process two aspects are to be defined. Firstly, every time a product is taken up for production, how much of it (production batch size, q) should be produced. Considering a large value of q favors the manufacturer while a small value of q suits the retailers. Secondly, for a given batch size q, at what level of retailer’s inventory (production queuing point), the batch size S of a product be taken up for production by the manufacturer. A higher value of S incurs more holding cost whereas a lower value of S increases the chance of backorder. A tradeoff between the holding and backorder cost must be taken into consideration while choosing an optimal value of S. It may be noted that due to multiple products and single processor, a product ‘taken’ up for production may not get the processor immediately, and may have to wait in a queue. The ‘S’ should factor in the probability of waiting time in the queue.

Energy sustainability in operations: an optimization study

In recent years, energy sustainability has attracted increasing attention from scholars, policy makers, and practitioners. However, in practical competitiveness, companies also have to maintain flexibility to deliver customer requirements. Existing theory has largely seen flexibility and sustainability as separate issues, ignoring their relationship. To address this gap, we attempt to explore energy sustainability using flexible operations management practices in India. Following a case research methodology, we investigate how flexible operations management practices embraced by a leading organization that minimizes energy consumption in plants with better resource utilization and better quality of work life for staff to meet the future requirements based on market forecast. We solve a real-time multi-criteria strategic capacity planning problem with multiple objectives such as throughput maximization, waste minimization, and resource utilization maximization by using discrete event simulation technique. Our data stems from ongoing research within a car manufacturing company in India, located at Pune. Shift timings, setup time, production batch size, and differential rated capacities of plants are considered as the variables in this study. However, optimal routing flexibility combined with volume flexibility helps to achieve significant reduction in energy consumption. Finally, we offer future research questions on flexible operations management practices.

A methodology for evaluating the influence of batch size and part geometry on the environmental performance of machining and forming processes

Metallic material processing plays a significant role in terms of global environmental impact. As a result, energy- and resource-efficient strategies in the metal shaping technology domain need to be identified urgently. Recently, the scientific world has been paying more and more attention to the environmental impact analysis of manufacturing processes. Despite this increased attention, the state of the art in the domain of environmental impact analysis of metal shaping processes is still characterized by gaps in knowledge and in methodologies. In particular, metal forming processes are still not well documented, in terms of their environmental impact, and there is a lack of systematic and comparative approaches. This paper offers a contribution to a better understanding of the environmental impact of forming and machining processes. A thorough methodology has been developed to take into due account all the environmental factors of influence on both of the considered technologies. A comparative analysis has been performed, varying the production batch size and part geometry. The importance of an environmental performance analysis that can include both batch size and geometry variations is discussed and highlighted throughout the paper. Finally, the application of the proposed methodology has resulted in the setting up of an eco-design tool, here defined as a Process Sustainability Diagram (PSD), which allows the greenest technology to be selected for the analyzed case study, while varying the considered factors of influence.

Effect of Changing Operating Policies on Energy Use Consumption

Energy efficiency continues to be an important focus in manufacturing in light of energy cost, environmental concerns and legislations. Reducing energy use is essential for maintaining manufacturing sustainability and competitiveness. This paper proposes a methodology for energy use analysis that employs analytical, simulation and statistical tools for the purpose of investigating the effect of changing operating strategies such as production scheduling and batch sizes on manufacturing line total energy use. The proposed methodology identifies potential energy savings and guides improvement efforts. A real case study of an automotive OEM supplier, which experiences system changes as new products are introduced is presented. The main pieces of equipment consuming energy in the entire production line were identified and the total energy consumption per product was estimated. The manufacturing line was modelled using discrete event simulation, and the effect on the line total energy consumption of different operating strategies including different batch sizes and production schedules were determined. Analysis of Variance (ANOVA) was employed to analyze the effects of each operating strategy on the energy usage. The results reveal a relationship between changes in the operating strategies and energy use. This study demonstrated that optimizing the production line operating strategies can potentially lead to significant energy savings without the need for major modifications of equipment or machine setups. Practical examples which can guide industrial energy management practitioners in planning, assessing and improving manufacturing systems efficiently are provided. This study emphasizes the importance of including energy use data in manufacturing systems operating policy decisions.

Additive manufacturing–integrated hybrid manufacturing and subtractive processes: economic model and analysis

This article presents economic models for a new hybrid method where additive manufacturing (AM) and subtractive methods (SMs) are integrated through composite process planning. Although AM and SM offer several unique advantages, there are technological limitations such as tolerance and surface finish requirements; tooling and fixturing, etc. that cannot be met by a single type of manufacturing. The intent of this article is not to show a new manufacturing method, but rather to provide economic context to additive and subtractive methods as the best practice provides, and look at the corresponding economics of each of those methods as a function of production batch size, machinability, cost of the material, part geometry and tolerance requirements. Basic models of fixed and variable costs associated with additive, subtractive and hybrid methods to produce parts are also presented. An experimental design is used to study the influence of production volume, material and operating cost, batch size, machinability of the material and impact of reducing AM processing time. A composite response model for the unit cost is computed for the various levels associated with such engineering requirements. The developed models provide insight into how these variables affect the costs associated with engineering a mechanical product that will be produced using AM and SM methods. From the results, it appears that batch size, AM processing time and AM processing cost were the major cost factors. It was shown that the cost of producing ‘near-net’ shape through SM and AM was the decision criteria; which will be critical for tough-to-machine alloys and at multi-batch size.

Adaptive model predictive inventory controller for multiproduct batch plant

An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at order intervals and in order quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model predictive control algorithm was developed that uses a periodic square wave model to represent the flows of the material between the processes and the storage units. The boundedness of the control output and the convergence of the estimated parameters in implementations of the proposed algorithm were mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of this approach was demonstrated by performing simulations. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1867–1880, 2015

Towards Virtual Presence Based on Multimodal Man-Machine Communication: A Remote Operation Support System for Industrial Robots

Worldwide, the focus on industrial development is on achieving smaller and smaller batch sizes in production. This has led to the need for providing cost efficient support to robot users. In this study, a new method for the remote operation of industrial robot systems is introduced. The method is based upon multi-modal man-machine communication and sensor bridging to transfer huge amounts of information from the industrial robot cell to the brain of the remote operator.

Analysis of Traceability Optimization and Shareholder’s Profit for Efficient Supply Chain Operation under Product Recall Crisis

Product recall gains considerable importance in recent times; the reason may be the huge losses faced by manufacturers because of product recall issues. Furthermore, the revenue of the firm is immensely affected as a result of product recall, which may lead to serious outcomes. Huge recall cost (such as repairing or destroying the recalled products and cost of notification) occurs as a result of large recall. Therefore, in order to minimize the quantity and probability of recalls the traceability systems are widely used and considered as a necessary part of product safety strategies. However, from literature it is clear that manufacturers are still struggling to obtain the significant results. This study helps the managers to understand the importance of recall cost by analysing its impact on shareholders profit. Keeping in view the importance of problem, the paper proposed an integrated optimization model to minimize the expected loss to shareholders in recall crisis using batch dispersion methodology. The analysed results show that reduction in traceability level increases the expected shareholders losses while decreasing the operational costs. This will help managers to optimally set the production batch sizes in order to reduce the product recall impact.

The Epq Model With Emergency Maintenance, Imperfect Products, Backorders And Work In Process Inventory

Abstract. The Economic Production Quantity (EPQ) is one of the most popular models in inventory control environments. Although this model has been developed influenced by many factors, but in some areas it has received little attention that the examination of the machine failure is one. In this paper, taking into account the assumptions such as the possibility of producing imperfect products with the ability to re-work and without the ability to rework and etc. the EPQ model has been developed in applying the policy of Emergency Maintenance (EM) and a method for achieving optimal size of production batch has been offered. The results of the model sensitivity analysis show that work in process (WIP) inventory holding cost rate while having an inverse relationship with the optimum batch size production, has the greatest impact in determining the amount.

An experimental comparison of film flow parameters and cleaning behaviour of falling liquid films for different tilt angles

Efficient cleaning is crucial in the food industry due to increasing safety needs, cost pressure and diminishing size of production batches. In Cleaning in Place (CIP) systems different flow types provide the cleaning effect by interaction, wherein falling liquid films make a significant contribution to cleaning. The flow of falling films can be significantly influenced by the design of the surface and, for example, of the tilt angle. In a previous publication it was shown for a low tilt angle of 30° that for resource efficient cleaning a low wetting rate is suitable. It was pointed out that mean wall shear stress and mean flow velocity have an influence on the cleaning progress. In this paper the investigations are extended to describe the influence of the surface inclination in the range of 30–90° on the flow and cleaning behavior of zinc sulfide/Xanthan gum soil layers on stainless steel for film Reynolds numbers of 1190-3110. The results show that the tilt angle has a major impact on the film thickness, wall shear stress and the mean cleaning rate. Cleaning results for three different stainless steel surfaces show that a low surface roughness does not necessarily lead to a better cleaning result.

A Simulated Annealing Algorithm for Machine Cell Formation Under Uncertain Production Requirements

Due to customized products, shorter product life cycles, and unpredictable patterns of demand, the manufacturing industries are faced with stochastic production requirements. The production requirements (product mix and demand) may not be known exactly at the time of designing the manufacturing cell. It is likely that a set of possible production requirements (scenarios) with certain probabilities may be given at the design stage. Though a large number of research works on manufacturing cell have been reported, very few of them have considered random product mix constraint at the design stage. This paper presents a nonlinear mixed-integer mathematical model for the cell formation problem with the uncertainty of the product mix for a single period. The model incorporates real-life parameters such as alternate routing, operation sequence, duplicate machines, uncertain product mix, uncertain product demand, batch size, processing time, machine capacity, and various cost factors. The cost factors considered are machine amortization costs, operating costs, inter-cellular material handling costs, and intra-cellular material handling costs. A consultancy work is carried out for the proposed auto-components manufacturing industry to be located in the suburb of Chennai. In this paper, a solution methodology for best possible cell formation using simulated annealing algorithm is presented and the computational procedure has been illustrated for the case study undertaken.

자동차 부품산업의 고객 요구 납기 충족을 위한 적정재고 수준에 관한 시뮬레이션 연구

Auto part industry supplies production for auto manufacturer and after market. These company have inventory for delivery. High inventory level can be good for delivery, but cost will be increase. Low inventory level can be customer unsatisfaction for delivery late. Low inventory level also is reason of low productivity by decreasing product batch size. These article suggest model for calculation a proper inventory level and prove a effect by simulation of some company.

Buyer–vendor coordination for fixed lifetime product with quantity discount under finite production rate

Buyer–vendor coordination has been widely addressed; however, the fixed lifetime of the product is seldom considered. In this paper, we study the coordination of an integrated production-inventory system with quantity discount for a fixed lifetime product under finite production rate and deterministic demand. We first derive the buyer’s ordering policy and the vendor’s production batch size in decentralised and centralised systems. We then compare the two systems and show the non-coordination of the ordering policies and the production batch sizes. To improve the supply chain efficiency, we propose quantity discount contract and prove that the contract can coordinate the buyer–vendor supply chain. Finally, we present analytically tractable solutions and give a numerical example to illustrate the benefits of the proposed quantity discount strategy.

Risk Assessment of Non-repetitive Production Processes

The purpose of this paper is to support the process of production batch size definition in non-repetitive production like MTO or ETO based on reliability assessment. Although theory supposes the production batch size to be equal to customer order, situation observed in real production systems is different. Production often protects itself against manufacturing process unreliability resulting in non-conformed parts with overproduction. To manage the production wisely, the process risk should be evaluated. High ratio of setup costs to unit production costs should be taken into consideration as well as potential risk of each operation. This paper describes a proposed way, coming from the idea of FMEA methodology, to assess the risk and calculate costs related to process unreliability. The result then supports the batch size definition.

Selective proper orthogonal decomposition model reduction for forming simulations

AbstractRobot‐based incremental sheet metal forming is a cost‐effective and flexible method for prototype and low batch size production. The simulation of such processes is very challenging and elaborate from the computational point of view. To reduce the computational effort model reduction techniques such as proper orthogonal decomposition (POD) can be applied. But the reduction of highly non‐linear models in solid mechanics for example forming simulation still leads to problems of efficiency and accuracy. Therefore, the aim of this paper is to present an alternative way to use POD for forming processes. The presented selective POD (SPOD) method is used to split the model into two domains depending on the degree of plastic strain. Only the domain with approximately linear elastic behavior will be reduced by using POD. Utilizing the SPOD method for the example of forming a horizontal flute reduces the computational time up to around 30 per cent. High accuracy with approximation errors smaller than one per mill is achieved. (© 2013 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Logical reconfiguration of reconfigurable manufacturing systems with stream of variations modelling: a stochastic two-stage programming and shortest path model

This article studies logical reconfiguration of reconfigurable manufacturing systems (RMS) to minimise production lead times and buffer inventory level when the process variations and worker utilisation are considered. Since the RMS must be flexible for different job orders, the design of RMS requires diagnostic methodology and stream of variations (SoV) theory for rapid ramp-up in order to control the process variations that might occur as time goes on. The flexibility of the manufacturing systems is represented by logical elements of RMS in terms of changeable production batch size. The three phases solution is proposed by (1) utilising SoV modelling to find the allowable production lead times, (2) finding the optimum buffer stock level and production capacity at changeable production batch size and (3) finding worker routings at optimum worker utilisation. Monte carlo simulation is employed at Phase 1 to get the optimum production lead times, Phase 2 decision is formulated as a stochastic two-stages programming where buffer inventory level (first stage decison) has to be established prior to changeable production batching at future period and shortest path problems (SPP) algorithm is used to find an optimum worker routing at Phase 3. A serial inventory production (SIP) is used as an example to answer the following research questions: (1) What is the impact of SoV on both buffer inventory allocation and worker routings? (2) When is logical reconfiguration most beneficial in improving SIP profitability? (3) What is the impact of logical reconfiguration on both cost and lead time reduction? Three instances are used to investigate the effect of logical reconfiguration on the different structure of SIP systems. The results and analysis indicate that consideration of SoV is capable of increasing the profit, reducing operation lead times and maximising worker utilisation. Finally, management decision-making is discussed among other concluding remarks.

Batch Size Determination: A Case Study of Hard Disk Drive Industry

This paper gives an attempt in determining an appropriate production batch size by using simulation method. The case study of this paper is an industry producing hard disk drives in Thailand. The scope of the production process considered in this paper is in the backend area of the production process of hard disk drives. Three main customers are considered. Each customer requires different sample size in an inspection process and the sample size affects bath size. Therefore, this paper needs to consider each customer separately and tries to determine an appropriate batch size for each customer. First, data collection of the process and its details are given. The as-is simulation model is constructed. Outputs, production lead time, work in process and machine utilization are used to verified the as-is model. The statistical method is used to compare between those in the real system and those based on the model. Further three levels of batch sizes are analyzed by using the simulation model. It is found that each customer type requires different batch size.

Research on Capacity-Constrained Manufacturing/Remanufacturing Hybrid System with Outsourcing

The purpose of our research is to develop a decision-making model for manufacturing and remanufacturing hybrid system with the goal of maximizing corporate profit. Under a random market environment, we assumed that the remanufacturing operation was outsourced and both the manufacturing process and remanufacturing process had capacity constraints. By analyzing the relations between manufacturing/remanufacturing production batch sizes and inventory level, we established a multi-period capacitated manufacturing/remanufacturing hybrid decision-making model. A heuristic genetic algorithm was designed and a numerical example demonstrated the validity of the model and the algorithm. We conclude that the manufacturing/remanufacturing quantities decision is affected by both market parameters and capacity limitations. This study is useful for manufacturers to make strategy and plan in manufacturing/remanufacturing operations.

Production–shipment policy for EPQ model with quality assurance and an improved delivery schedule

This article is concerned with determining the production–shipment policy for an economic production quantity model with quality assurance and an improved delivery schedule. We extend a recent work by Chiu et al. [Y.-S.P. Chiu, C.-A.K. Lin, H.-H. Chang, and V. Chiu, Mathematical modeling for determining economic batch size and optimal number of deliveries for EPQ model with quality assurance, Math. Comput. Model. Dyn. Sys. 16 (4) (2010), pp. 373–388] by incorporating an alternative delivery plan that aims at lowering the inventory holding cost for both supplier and buyer in such an integrated inventory system. Mathematical modelling along with Hessian matrix equations is used, and as a result the optimal production batch size and optimal number of deliveries are derived. A numerical example is provided to demonstrate the practical use of the results and the significant savings in stock holding costs for both vendor and buyer.